A typology of collaborative research networks
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Bibliographic record
Abstract
Purpose Many studies have investigated how the structure of the collaborative networks of researchers influences the nature of their work, and its outcome. Co-authorship networks (CANs) have been widely looked at as proxies that can help bring understanding to the structure of research collaborative ties. The purpose of this paper is to provide a framework for describing what influences the formation of different research collaboration patterns. Design/methodology/approach The authors use social network analysis (SNA) to analyze the co-authorship ego networks of the ten most central authors in 24 years of papers (703 papers and 1,118 authors) published in the Proceedings of CASCON, a computer science conference. In order to understand what lead to the formation of the different CANs the authors examined, the authors conducted semi-structured interviews with these authors. Findings Based on this examination, the authors propose a typology that differentiates three styles of co-authorship: matchmaking, brokerage, and teamwork. The authors also provide quantitative SNA-based measures that can help place researchers’ CAN into one of these proposed categories. Given that many different network measures can describe the collaborative network structure of researchers, the authors believe it is important to identify specific network structures that would be meaningful when studying research collaboration. The proposed typology can offer guidance in choosing the appropriate measures for studying research collaboration. Originality/value The results presented in this paper highlight the value of combining SNA analysis with interviews when studying CAN. Moreover, the results show how co-authorship styles can be used to understand the mechanisms leading to the formation of collaborative ties among researchers. The authors discuss several potential implications of these findings for the study of research collaborations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it